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Qualitative data analysis: a practical example
Helen Noble,1 Joanna Smith2
The aim of this paper is to equip readers with an under-
standing of the principles of qualitative data analysis
and offer a practical example of how analysis might be
undertaken in an interview-based study.
What is qualitative data analysis?Qualitative research is a generic term that refers to a
group of methods, and ways of collecting and analysing
data that are interpretative or explanatory in nature and
focus on meaning. Data collection is undertaken in the
natural setting, such as a clinic, hospital or a partici-
pants home because qualitative methods seek to
describe, explore and understand phenomena from the
perspective of the individual or group. Reality is cocon-
structed by the research participants and the researcher,
with the depth of data collected more important than
recruiting large samples. The individual interview
method is the most widely used method of data collec-tion in qualitative research and a range of data can be
collected including eld notes, audio and video record-
ings, images or documents. Qualitative researchers
usually work with text when analysing data; data can
be transcribed in entirety or focus on selected sections.
However, focusing on selected sections of the data may
not capture the nuances of observations or participants
descriptions and may fragment the data. The challenge
for qualitative researchers is to present a cohesive repre-
sentation of the data, which can be vast and messy,1
and needs to make sense of diverse viewpoints or
complex issues. The process of data analysis is to assem-
ble or reconstruct the data in a meaningful or compre-
hensible fashion, in a way that is transparent, rigorous
and thorough, while remaining true to participants
accounts.
What are the approaches in undertakingqualitative data analysis?Although qualitative data analysis is inductive and
focuses on meaning, approaches in analysing data are
diverse with different purposes and ontological (con-
cerned with the nature of being) and epistemological
(knowledge and understanding) underpinnings.2
Identifying an appropriate approach in analysing
qualitative data analysis to meet the aim of a study can
be challenging. One way to understand qualitative data
analysis is to consider the processes involved.3
Approaches can be divided into four broad groups: qua-
sistatistical approaches such as content analysis; the use
of frameworks or matrices such as a framework
approach and thematic analysis; interpretative
approaches that include interpretative phenomenological
analysis and grounded theory; and sociolinguistic
approaches such as discourse analysis and conversation
analysis. However, there are commonalities across
approaches. Data analysis is an interactive process,
where data are systematically searched and analysed in
order to provide an illuminating description of phenom-
ena; for example, the experience of carers supporting
dying patients with renal disease4 or student nurses
experiences following assignment referral.5 Data ana-
lysis is an iterative or recurring process, essential to thecreativity of the analysis, development of ideas, clarify-
ing meaning and the reworking of concepts as new
insights emergeor are identied in the data.
Do you need data software packages whenanalysing qualitative data?Qualitative data software packages are not a prerequisite
for undertaking qualitative analysis but a range of pro-
grammes are available that can assist the qualitative
researcher. Software programmes vary in design and
application but can be divided into text retrievers, code
and retrieve packages and theory builders.6 NVivo and
NUD*IST are widely used because they have sophisti-
cated code and retrieve functions and modelling cap-
abilities, which speed up the process of managing large
data sets and data retrieval. Repetitions within data can
be quantied and memos and hyperlinks attached to
data. Analytical processes can be mapped and tracked
and linkages across data visualised leading to theory
development.6 Disadvantages of using qualitative data
software packages include the complexity of the soft-
ware and some programmes are not compatible with
standard text format. Extensive coding and categorising
can result in data becoming unmanageable and
Table 1 Data extract containing units of data and line-by-line coding
Data extract (carer) units of data (in vivo codes highlighted)Early descriptive codes/line-by-linecoding
He (the doctor) said there was nothing more he could do for her. I said to him,cant you give her dialysis?And he said,no because it would kill her. I supposeits too late in the day. I dont know. Thats the reason he gave me, it would killher.So I dont really know, but I thought well, why wait till theres only 20% functionleft before you tell me in the first place. So shouldn t he have told me when shecould have had dialysis?Shouldnt someone then have said to me, well look, shecan have dialysis before it got to the stage where she suddenly has 20% offunction and she cant have it. Couldnt someone have mentioned it earlier?Youknow what Im trying to say?
Nothing more they could do WantingdialysisTreatment would killToo late to treatTreatment would killNot being told early about prognosis/reduced kidney functionNot being involved in treatment decision/confusion*Missed treatment opportunity?
*This early description can be tracked through the following tables, essential in demonstrating transparency.
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10.1136/eb-2013-101603
1School of Nursing and
Midwifery, Queenss University
Belfast, Belfast, UK2Department of Health Sciences,
University of Hudderseld,
Hudderseld, UK
Correspondence to:
Dr Helen Noble
School of Nursing and
Midwifery, Queens University
Belfast, Medical Biology Centre,
97 Lisburn Road, Belfast BT9
7BL, UK;
2 Evid Based NursJanuary 2014| volume 17| number 1|
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researchers may nd visualising data on screen inhibits
conceptualisation of the data.
How do you begin analysing qualitativedata?Despite the diversity of qualitative methods, the subse-
quent analysis is based on a common set of principles
and for interview data includes: transcribing the inter-
views; immersing oneself within the data to gain
detailed insights into the phenomena being explored;developing a data coding system; and linking codes or
units of data to form overarching themes/concepts,
which may lead to the development of theory.2
Identifying recurring and signicant themes, whereby
data are methodically searched to identify patterns in
order to provide an illuminating description of a phe-
nomenon, is a central skill in undertaking qualitative
data analysis. Table 1 contains an extract of data taken
from a research study which included interviews with
carers of people with end-stage renal disease managed
without dialysis. The extract is taken from a carer who is
trying to understand why her mother was not offered
dialysis. The rst stage of data analysis involves the
process of initial coding, whereby each line of the data
is considered to identify keywords or phrases; these are
sometimes known as in vivo codes (highlighted) because
they retain participantswords.
When transcripts have been broken down into man-
ageable sections, the researcher sorts and sifts them,
searching for types, classes, sequences, processes, pat-
terns or wholes. The next stage of data analysis involves
bringing similar categories together into broader themes.
Table 2 provides an example of the early development
of codes and categories and how these link to form
broad initial themes.
Table 3 presents an example of further category
development leading to nal themes which link to anoverarching concept.
How do qualitative researchers ensure dataanalysis procedures are transparent androbust?In congruence with quantitative researchers, ensuring
qualitative studies are methodologically robust is essen-
tial. Qualitative researchers need to be explicit in
describing how and why they undertook the research.
However, qualitative research is criticised for lacking
transparency in relation to the analytical processes
employed, which hinders the ability of the reader to crit-
ically appraise study ndings.7 In the three tables pre-
sented the progress from units of data to coding to
theme development is illustrated. Not involved in treat-
ment decisions appears in each table and informs one
of the nal themes. Documenting the movement from
units of data to nal themes allows for transparency of
data analysis. Although other researchers may interpret
the data differently, appreciating and understanding
how the themes were developed is an essential part of
demonstrating the robustness of the ndings. Qualitative
researchers must demonstrate rigour, associated with
openness, relevance to practice and congruence of the
methodological approch.2 In summary qualitative
research is complex in that it produces large amounts of
data and analysis is time consuming and complex.
High-quality data analysis requires a researcher with
expertise, vision and veracity.
Competing interests None.
References1. Lee BA real life guide to accounting research: a behind the
scenes view of using qualitative research methods. Amsterdam:
Elsevier, 2004.
2. Morse JM, Richards L.Read merst for a users guide to
qualitative methods. London: Sage Publications, 2002.
3. Smith J, Cheater F, Bekker H. Theoretical versus pragmatic
design challenges in qualitative research.Nurse Res
2011;18:3951.
4. Noble H,Kelly D, Hudson P. Experiences of carers supporting
dying renal patients, managed without dialysis.J Adv Nurs
2013;69:182939.
5. Robshaw M,Smith J. Keeping aoat: student nurses
experiences following assignment referral.Nurse Educ Today
2004;24:51120.
6. McLafferty E, Farley AH. Analysing qualitative data using
computer software.Nurs Times 2006;102:346.
7. Maggs-Rapport F. Best research practice: in pursuit of
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Table 2 Development of initial themes from descriptivecodes
Early descriptive codes/categories Broad initial theme
Wanting dialysisNo benefit from treatmentsNot involved in treatment decisions*Poor understanding of diseasemanagementConfusion about treatmentsNot sure about which treatmentoptions to takeRequiring further knowledgeInadequate communication
The less informeddecision
*This early description can be tracked through the followingtables, essential in demonstrating transparency.
Table 3 Development of final themes and overarchingconcept
Categorydevelopment Final themes
Overarchingconcepts
Arduous nature ofdialysis
Informed andautonomousdecisions
The patientsdecision
Difficulties in gettingto hospital
Previous experienceof dialysis
Age as a reason notto start dialysis
Uncertainty abouttreatment options
Less informeddecisions
Not involved intreatment decisions*
Poor understandingof diseasemanagement
Confusion abouttreatments
*This early description can be tracked through the followingtables, essential in demonstrating transparency.
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doi: 10.1136/eb-2013-1016032013
2014 17: 2-3 originally published online November 20,Evid Based NursHelen Noble and Joanna SmithexampleQualitative data analysis: a practical
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